A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.
Report
generated on 2025-02-19, 00:13 CET
based on data in:
/vulpes/proj/ngis/ngi2016004/private/strategic_proj/SR_23_02_Element_vs_Illumina/analysis/differential_coverage
General Statistics
| Sample Name | ≥ 30X | Median | Mean Cov. | Median Coverage | Bases ≥ 30X | Reads mapped |
|---|---|---|---|---|---|---|
| aviti_hq_KMS12BM | 0.4% | 9.0X | 9.4 | |||
| aviti_hq_KMS12BM.collect_wgs_metrics | 50.0X | 81% | ||||
| aviti_hq_KMS12BM.downsampled | 217.3M | |||||
| aviti_hq_MM1S | 0.1% | 10.0X | 9.3 | |||
| aviti_hq_MM1S.collect_wgs_metrics | 50.0X | 86% | ||||
| aviti_hq_MM1S.downsampled | 218.0M | |||||
| aviti_hq_OPM2 | 0.3% | 9.0X | 9.4 | |||
| aviti_hq_OPM2.collect_wgs_metrics | 48.0X | 87% | ||||
| aviti_hq_OPM2.downsampled | 217.8M | |||||
| aviti_hq_REH | 0.1% | 10.0X | 9.4 | |||
| aviti_hq_REH.collect_wgs_metrics | 45.0X | 87% | ||||
| aviti_hq_REH.downsampled | 217.6M | |||||
| aviti_ngi_KMS12BM | 0.4% | 9.0X | 9.3 | |||
| aviti_ngi_KMS12BM.collect_wgs_metrics | 17.0X | 7% | ||||
| aviti_ngi_KMS12BM.downsampled | 222.9M | |||||
| aviti_ngi_MM1S | 0.1% | 9.0X | 9.3 | |||
| aviti_ngi_MM1S.collect_wgs_metrics | 17.0X | 4% | ||||
| aviti_ngi_MM1S.downsampled | 223.6M | |||||
| aviti_ngi_OPM2 | 0.3% | 9.0X | 9.3 | |||
| aviti_ngi_OPM2.collect_wgs_metrics | 19.0X | 9% | ||||
| aviti_ngi_OPM2.downsampled | 223.7M | |||||
| aviti_ngi_REH | 0.1% | 10.0X | 9.3 | |||
| aviti_ngi_REH.collect_wgs_metrics | 15.0X | 1% | ||||
| aviti_ngi_REH.downsampled | 222.6M | |||||
| xplus_sns_KMS12BM | 0.4% | 9.0X | 9.6 | |||
| xplus_sns_KMS12BM.collect_wgs_metrics | 50.0X | 82% | ||||
| xplus_sns_KMS12BM.downsampled | 285.0M | |||||
| xplus_sns_MM1S | 0.1% | 10.0X | 9.5 | |||
| xplus_sns_MM1S.collect_wgs_metrics | 50.0X | 87% | ||||
| xplus_sns_MM1S.downsampled | 289.7M | |||||
| xplus_sns_OPM2 | 0.4% | 10.0X | 9.5 | |||
| xplus_sns_OPM2.collect_wgs_metrics | 55.0X | 92% | ||||
| xplus_sns_OPM2.downsampled | 289.7M | |||||
| xplus_sns_REH | 0.1% | 10.0X | 9.6 | |||
| xplus_sns_REH.collect_wgs_metrics | 46.0X | 89% | ||||
| xplus_sns_REH.downsampled | 295.6M |
mosdepth
mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.
Cumulative coverage distribution
Proportion of bases in the reference genome with, at least, a given depth of coverage. Calculated across the entire genome length
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).
Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).
For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.
Coverage distribution
Proportion of bases in the reference genome with a given depth of coverage. Calculated across the entire genome length
For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).
Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.
If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).
This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).
Average coverage per contig
Average coverage per contig or chromosome
XY coverage
Picard
Picard is a set of Java command line tools for manipulating high-throughput sequencing data.
GC Coverage Bias
This plot shows bias in coverage across regions of the genome with varying GC content. A perfect library would be a flat line at y = 1.
WGS Coverage
The number of bases in the genome territory for each fold coverage. Note that final 1% of data is hidden to prevent very long tails.
WGS Filtered Bases
For more information about the filtered categories, see the Picard documentation.
Samtools
Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.
Flagstat
This module parses the output from samtools flagstat. All numbers in millions.